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The POLYGRAM NET™ Biofeedback Application is intended for the assessment and treatment of pelvic floor dysfunction through biofeedback in patients 4 years of age or older.

The POLYGRAM NET™ Biofeedback Application is used to assess and treat pelvic floor dysfunction. Data is collected in the anorectal canal, using sensors, and is displayed to the patient in a simple graphical format. The patient can modulate the activity of the anorectal muscles, thereby reeducating the pelvic floor muscles.

During a biofeedback study, a manometry catheter and/or EMG sensors are placed in the patient's anorectal canal at the sphincter. EMG sensors are then inserted anally and applied to the side of the anus. These EMG sensors, which are the same as used with the POLYGRAM Anorectal Function Testing Software (K000386), sense the patient's pelvic floor muscle activity during sequences of squeezing and relaxing maneuvers. These sensors and catheters are connected to the Medtronic Polygraf ID (K992713), which acquires the data to be displayed by the POLYGRAM NET™ Biofeedback Application.

The POLYGRAM NET™ Biofeedback Application displays a lane, that the patient navigates using their pelvic floor muscles. By squeezing and relaxing the pelvic floor muscles, the patient moves the signal level indicator up and down, trying to keep the signal level within the limits of the lane.

This document is a 510(k) summary for the Medtronic POLYGRAM NET™ Biofeedback Application, submitted to the FDA in 2004. It outlines the device description, intended use, and claims of substantial equivalence to predicate devices. However, it does not contain information about acceptance criteria or a study proving the device meets those criteria, as typically understood for performance testing related to accuracy, sensitivity, or specificity.

Here's a breakdown of why the requested information cannot be fully provided based on the input text:

The document explicitly states: "No applicable mandatory performance standards or special controls exist for this device." and "In-vitro testing was performed to support substantial equivalence to the predicate devices. In vitte testing was points indicate that the Medtronic POLYGRAM NET™ Biofeedback Application meets all of the design and performance requirements."

This indicates that the submission focuses on demonstrating that the new device functions similarly to previously cleared devices (predicate devices) and adheres to general design and performance requirements, rather than presenting a study against specific, quantified acceptance criteria for a novel clinical performance claim.

Therefore, many of your requested items, which are typical for clinical performance studies, are not present.

Here's what can be extracted and what cannot:

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not applicable. The document states "In-vitro testing was performed". This is laboratory-based testing, not human-subjects testing that would involve a test set with data provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not applicable, as no human-subjects "test set" for clinical performance is described.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable, as no human-subjects "test set" for clinical performance is described.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
Not applicable. This device is a biofeedback application, not an AI or imaging diagnostic tool that would typically involve MRMC studies or human reader improvement with AI assistance. It aids patients in modulating muscle activity.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
This is a biofeedback application designed for human-in-the-loop interaction (patient modulating muscles based on software display). The "in-vitro testing" mentioned would be analogous to standalone testing, focusing on the software's ability to accurately process sensor data and display it, but not against clinical outcomes or diagnoses.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not specified, as the testing was in-vitro. For in-vitro testing, the "ground truth" would likely be known electrical or physiological signals generated by testing equipment, or calibrated sensor inputs, to verify the system accurately acquires and displays data.

8. The sample size for the training set
Not applicable. This document describes a traditional medical device submission, not a machine learning or AI algorithm where a "training set" would be explicitly used in the context of model development.

9. How the ground truth for the training set was established
Not applicable, as no "training set" is mentioned in the context of machine learning.

Summary of the study that proves the device meets the acceptance criteria (based on the provided text):

The device's acceptance was based on in-vitro testing to confirm it met its "design and performance requirements" and was "substantially equivalent" to predicate devices. The nature of these "design and performance requirements" is not detailed beyond implying that the device functions as intended for acquiring and displaying biofeedback data. The 510(k) submission largely relies on demonstrating that the new device does not raise new questions of safety or effectiveness compared to devices already on the market with similar technological characteristics and intended uses. No clinical performance study against specific, quantified acceptance criteria (like accuracy, sensitivity, specificity) is described.

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